4 papers
Explaining deep learning for ECG using time-localized clusters
Ahcène Boubekki, Konstantinos Patlatzoglou, Joseph Barker +2
Deep learning has significantly advanced electrocardiogram (ECG) analysis, enabling automatic annotation, disease screening, and prognosis beyond traditional clinical capabilities.…
Data distribution impacts the performance and generalisability of contrastive learning-based foundation models of electrocardiograms
Gul Rukh Khattak, Konstantinos Patlatzoglou, Joseph Barker +15
Contrastive learning is a widely adopted self-supervised pretraining strategy, yet its dependence on cohort composition remains underexplored. We present Contrasting by Patient Aug…
Online Graph Topology Learning via Time-Vertex Adaptive Filters: From Theory to Cardiac Fibrillation
Alexander Jenkins, Thiernithi Variddhisai, Ahmed El-Medany +2
Graph Signal Processing (GSP) provides a powerful framework for analysing complex, interconnected systems by modelling data as signals on graphs. While recent advances have enabled…
Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements
Alexander Jenkins, Andrea Cini, Joseph Barker +10
Catheter ablation of Atrial Fibrillation (AF) consists of a one-size-fits-all treatment with limited success in persistent AF. This may be due to our inability to map the dynamics…